A New Methodology for Early Detection of Failures in Lithium-Ion Batteries
Mario Eduardo Carbonó dela Rosa,
Graciela Velasco Herrera,
Rocío Nava,
Enrique Quiroga González,
Rodolfo Sosa Echeverría,
Pablo Sánchez Álvarez,
Jaime Gandarilla Ibarra,
Víctor Manuel Velasco Herrera
Affiliations
Mario Eduardo Carbonó dela Rosa
Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 3462580, Mexico
Graciela Velasco Herrera
Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, Mexico City 04510, Mexico
Rocío Nava
Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 3462580, Mexico
Enrique Quiroga González
Instituto de Física, Benemérita Universidad Autónoma de Puebla (BUAP), Puebla 72570, Mexico
Rodolfo Sosa Echeverría
Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico
Pablo Sánchez Álvarez
Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico
Jaime Gandarilla Ibarra
Facultad de Ingeniería, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico
Víctor Manuel Velasco Herrera
Instituto de Geofísica, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico
The early fault detection and reliable operation of lithium-ion batteries are two of the main challenges the technology faces. Here, we report a new methodology for early failure detection in lithium-ion batteries. This new methodology is based on wavelet spectral analysis to detect overcharge failure in batteries that is performed for voltage data obtained in cycling tests, subjected to a standard charge/discharge protocol. The main frequencies of the voltage temporal signal, the harmonic components in the regular cycling test, and a low frequency pattern were identified. For the first time, battery failure can be anticipated by wavelet spectral analysis. These results could be the key to the new early detection of battery failures in order to reduce out-of-control explosions and fire risks.
